IEEE transactions on visualization and computer graphics最新文献_第7页

Utilizing Provenance as an Attribute for Visual Data Analysis: A Design Probe with ProvenanceLens. 利用来源作为视觉数据分析的属性：一个具有来源属性的设计探索。

IEEE transactions on visualization and computer graphics Pub Date : 2025-05-19 DOI: 10.1109/TVCG.2025.3571708

Arpit Narechania, Shunan Guo, Eunyee Koh, Alex Endert, Jane Hoffswell

{"title":"Utilizing Provenance as an Attribute for Visual Data Analysis: A Design Probe with ProvenanceLens.","authors":"Arpit Narechania, Shunan Guo, Eunyee Koh, Alex Endert, Jane Hoffswell","doi":"10.1109/TVCG.2025.3571708","DOIUrl":"https://doi.org/10.1109/TVCG.2025.3571708","url":null,"abstract":"Analytic provenance can be visually encoded to help users track their ongoing analysis trajectories, recall past interactions, and inform new analytic directions. Despite its significance, provenance is often hardwired into analytics systems, affording limited user control and opportunities for self-reflection. We thus propose modeling provenance as an attribute that is available to users during analysis. We demonstrate this concept by modeling two provenance attributes that track the recency and frequency of user interactions with data. We integrate these attributes into a visual data analysis system prototype, ProvenanceLens, wherein users can visualize their interaction recency and frequency by mapping them to encoding channels (e.g., color, size) or applying data transformations (e.g., filter, sort). Using ProvenanceLens as a design probe, we conduct an exploratory study with sixteen users to investigate how these provenance-tracking affordances are utilized for both decision-making and self-reflection. We find that users can accurately and confidently answer questions about their analysis, and we show that mismatches between the user's mental model and the provenance encodings can be surprising, thereby prompting useful self-reflection. We also report on the user strategies surrounding these affordances, and reflect on their intuitiveness and effectiveness in representing provenance.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144103437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CreativeSynth: Cross-Art-Attention for Artistic Image Synthesis With Multimodal Diffusion. CreativeSynth：跨艺术关注与多模态扩散的艺术图像合成。

IEEE transactions on visualization and computer graphics Pub Date : 2025-05-16 DOI: 10.1109/TVCG.2025.3570771

Nisha Huang, Weiming Dong, Yuxin Zhang, Fan Tang, Ronghui Li, Chongyang Ma, Xiu Li, Tong-Yee Lee, Changsheng Xu

{"title":"CreativeSynth: Cross-Art-Attention for Artistic Image Synthesis With Multimodal Diffusion.","authors":"Nisha Huang, Weiming Dong, Yuxin Zhang, Fan Tang, Ronghui Li, Chongyang Ma, Xiu Li, Tong-Yee Lee, Changsheng Xu","doi":"10.1109/TVCG.2025.3570771","DOIUrl":"10.1109/TVCG.2025.3570771","url":null,"abstract":"Although remarkable progress has been made in image style transfer, style is just one of the components of artistic paintings. Directly transferring extracted style features to natural images often results in outputs with obvious synthetic traces. This is because key painting attributes including layout, perspective, shape, and semantics often cannot be conveyed and expressed through style transfer. Large-scale pretrained text-to-image generation models have demonstrated their capability to synthesize a vast amount of high-quality images. However, even with extensive textual descriptions, it is challenging to fully express the unique visual properties and details of paintings. Moreover, generic models often disrupt the overall artistic effect when modifying specific areas, making it more complicated to achieve a unified aesthetic in artworks. Our main novel idea is to integrate multimodal semantic information as a synthesis guide into artworks, rather than transferring style to the real world. We also aim to reduce the disruption to the harmony of artworks while simplifying the guidance conditions. Specifically, we propose an innovative multi-task unified framework called CreativeSynth, based on the diffusion model with the ability to coordinate multimodal inputs. CreativeSynth combines multimodal features with customized attention mechanisms to seamlessly integrate real-world semantic content into the art domain through Cross-Art-Attention for aesthetic maintenance and semantic fusion. We demonstrate the results of our method across a wide range of different art categories, proving that CreativeSynth bridges the gap between generative models and artistic expression. Code and results are available at https://github.com/haha-lisa/CreativeSynth.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144083034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unsupervised Learning of Global Object-Centric Representations for Compositional Scene Understanding. 面向合成场景理解的全局对象中心表示的无监督学习。

IEEE transactions on visualization and computer graphics Pub Date : 2025-05-15 DOI: 10.1109/TVCG.2025.3570426

Tonglin Chen, Yinxuan Huang, Jinghao Huang, Bin Li, Xiangyang Xue

{"title":"Unsupervised Learning of Global Object-Centric Representations for Compositional Scene Understanding.","authors":"Tonglin Chen, Yinxuan Huang, Jinghao Huang, Bin Li, Xiangyang Xue","doi":"10.1109/TVCG.2025.3570426","DOIUrl":"https://doi.org/10.1109/TVCG.2025.3570426","url":null,"abstract":"The ability to extract invariant visual features of objects from complex scenes and identify the same objects in different scenes is inborn for humans. To endow AI systems with such capability, we introduce a novel compositional scene understanding method known as Compositional Scene understanding via Global Object-centric representations (CSGO). CSGO achieves comprehensive scene understanding, including the discovery and identification of objects, by leveraging a set of learnable global object-centric representations in an unsupervised manner. CSGO comprises three components: 1) Local Object-Centric Learning, which is responsible for extracting localized and scene-specific object-centric representations to discover objects; 2) Image Decoding, facilitating the reconstruction of object and scene images using the obtained object-centric representation as input; and 3) Global Object-Centric Learning, identifying the object across diverse scenes according to a set of learnable global object-centric representations, which indicates the scene-free intrinsic attributes (i.e., appearance and shape) of objects. Experimental results on three synthetic datasets and one real-world scene dataset demonstrate that CSGO has excellent object identification and attribute disentanglement abilities. Furthermore, the scene decomposition performance (indicating object discovery performance) of CSGO is superior to comparison methods.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144082307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Simulating Two-phase Fluid-rigid Interactions with an Overset-Grid Kinetic Solver. 用过置网格动力学求解器模拟两相流体-刚性相互作用。

IEEE transactions on visualization and computer graphics Pub Date : 2025-05-15 DOI: 10.1109/TVCG.2025.3570570

Xiaoyu Xiao, Ding Lin, Yiheng Wu, Kai Bai, Xiaopei Liu

{"title":"Simulating Two-phase Fluid-rigid Interactions with an Overset-Grid Kinetic Solver.","authors":"Xiaoyu Xiao, Ding Lin, Yiheng Wu, Kai Bai, Xiaopei Liu","doi":"10.1109/TVCG.2025.3570570","DOIUrl":"https://doi.org/10.1109/TVCG.2025.3570570","url":null,"abstract":"Simulating the coupled dynamics between rigid bodies and two-phase fluids, especially those with a large density ratio and a high Reynolds number, is computationally demanding but visually compelling with a broad range of applications. Traditional approaches that directly solve the Navier-Stokes equations often struggle to reproduce these flow phenomena due to stronger numerical diffusion, resulting in lower accuracy. While recent advancements in kinetic lattice Boltzmann methods for two-phase flows have notably enhanced efficiency and accuracy, challenges remain in correctly managing fluid-rigid boundaries, resulting in physically inconsistent results. In this paper, we propose a novel kinetic framework for fluid-rigid interaction involving two fluid phases. Our approach leverages the idea of an overset grid, and proposes a novel formulation in the two-phase flow context with multiple improvements to handle complex scenarios and support moving multi-resolution domains with boundary layer control. These new contributions successfully resolve many issues inherent in previous methods and enable physically more consistent simulations of two-phase flow phenomena. We have conducted both quantitative and qualitative evaluations, compared our method to previous techniques, and validated its physical consistency through real-world experiments. Additionally, we demonstrate the versatility of our method across various scenarios.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144081919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Low-error Reconstruction of Directional Functions with Spherical Harmonics. 球面谐波方向函数的低误差重构。

IEEE transactions on visualization and computer graphics Pub Date : 2025-05-15 DOI: 10.1109/TVCG.2025.3570092

Michal Vlnas, Tomas Milet, Pavel Zemcik

引用次数: 0

Handcrafted local feature descriptor-based point cloud registration and its applications: a review. 基于手工局部特征描述符的点云配准及其应用综述。

IEEE transactions on visualization and computer graphics Pub Date : 2025-05-14 DOI: 10.1109/TVCG.2025.3569894

Wuyong Tao, Ruisheng Wang, Xianghong Hua, Jingbin Liu, Xijiang Chen, Yufu Zang, Dong Chen, Dong Xu, Bufan Zhao

{"title":"Handcrafted local feature descriptor-based point cloud registration and its applications: a review.","authors":"Wuyong Tao, Ruisheng Wang, Xianghong Hua, Jingbin Liu, Xijiang Chen, Yufu Zang, Dong Chen, Dong Xu, Bufan Zhao","doi":"10.1109/TVCG.2025.3569894","DOIUrl":"https://doi.org/10.1109/TVCG.2025.3569894","url":null,"abstract":"Point cloud registration serves as a fundamental problem across multiple fields including computer vision, computer graphics, and remote sensing. While local feature descriptors (LFDs) have long been established as a cornerstone for point cloud registration and the LFD-based approach has been extensively studied, the field has witnessed significant advancements in recent years. Despite these developments, the research community lacks a systematic review to consolidate these contributions, leaving many researchers unaware of recent progress in LFD-based registration. To address this gap, we present a comprehensive review that critically examines both state-of-the-art and widely referenced methods across all subtasks of LFD-based registration. Our work provides: (1) an extensive survey of existing methodologies, (2) in-depth analysis of their respective strengths and limitations, (3) insightful observations and practical recommendations, and (4) a thorough summary of relevant applications and publicly available datasets. This systematic overview offers valuable guidance for researchers pursuing future investigations in this domain.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144083035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Virtual, Augmented, and Extended Reality Applied to Science Communication: A Systematic Literature Review. 虚拟，增强和扩展现实应用于科学传播：系统的文献综述。

IEEE transactions on visualization and computer graphics Pub Date : 2025-05-12 DOI: 10.1109/TVCG.2025.3569398

Juan Romero-Luis, Jose Luis Rubio-Tamayo, Alberto Sanchez-Acedo, Daniel Wuebben, Valeri Codesido-Linares

{"title":"Virtual, Augmented, and Extended Reality Applied to Science Communication: A Systematic Literature Review.","authors":"Juan Romero-Luis, Jose Luis Rubio-Tamayo, Alberto Sanchez-Acedo, Daniel Wuebben, Valeri Codesido-Linares","doi":"10.1109/TVCG.2025.3569398","DOIUrl":"https://doi.org/10.1109/TVCG.2025.3569398","url":null,"abstract":"Extended reality (XR)-which includes virtual reality (VR) and augmented reality (AR)-is becoming increasingly popular for sharing scientific knowledge. This research evaluates the state-of-the-art in XR for scientific communication. Our two-phase methodology began with a Systematic Literature Review, identifying 94 relevant articles and conference papers from the last decade (2013- 2023) sourced from the Web of Science and SCOPUS databases. These publications show scholars and practitioners using XR to convey scientific findings, foster awareness, ignite interest, shape opinions, and enhance understanding. In the second phase, we applied data clustering and analysis. Our findings highlight a significant increase in XR studies over the last decade, with the XR technologies used for communication (N = 24), dissemination (N = 23), educational/training (N = 21), and decision-making (N = 10). Our results indicate the need to establish clearer guidelines for aligning science communication and to create more possibilities to publish peer-reviewed research in.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144029357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MotionCrafter: Plug-and-Play Motion Guidance for Diffusion Models. motioncraft：即插即用的扩散模型运动指导。

IEEE transactions on visualization and computer graphics Pub Date : 2025-05-12 DOI: 10.1109/TVCG.2025.3568880

Yuxin Zhang, Weiming Dong, Fan Tang, Nisha Huang, Haibin Huang, Chongyang Ma, Pengfei Wan, Tong-Yee Lee, Changsheng Xu

{"title":"MotionCrafter: Plug-and-Play Motion Guidance for Diffusion Models.","authors":"Yuxin Zhang, Weiming Dong, Fan Tang, Nisha Huang, Haibin Huang, Chongyang Ma, Pengfei Wan, Tong-Yee Lee, Changsheng Xu","doi":"10.1109/TVCG.2025.3568880","DOIUrl":"https://doi.org/10.1109/TVCG.2025.3568880","url":null,"abstract":"The essence of a video lies in the dynamic motions. While text-to-video generative diffusion models have made significant strides in creating diverse content, effectively controlling specific motions through text prompts remains a challenge. By utilizing user-specified reference videos, the more precise guidance for character actions, object movements, and camera movements can be achieved. This gives rise to the task of motion customization, where the primary challenge lies in effectively decoupling the appearance and motion within a video clip. To address this challenge, we introduce MotionCrafter, a novel one-shot instance-guided motion customization method that is suitable for both pre-trained text-to-video and text-to-image diffusion models. MotionCrafter employs a parallel spatial-temporal architecture that integrates the reference motion into the temporal component of the base model, while independently adjusting the spatial module for character or style control. To enhance the disentanglement of motion and appearance, we propose an innovative dual-branch motion disentanglement approach, which includes a motion disentanglement loss and an appearance prior enhancement strategy. To facilitate more efficient learning of motions, we further propose a novel timestep-layered tuning strategy that directs the diffusion model to focus on motion-level information. Through comprehensive quantitative and qualitative experiments, along with user preference tests, we demonstrate that MotionCrafter can successfully integrate dynamic motions while maintaining the coherence and quality of the base model, providing a wide range of appearance generation capabilities. MotionCrafter can be applied to various personalized backbones in the community to generate videos with a variety of artistic styles.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144059189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Critical Analysis of the Usage of Dimensionality Reduction in Four Domains. 对四个领域降维用法的批判性分析。

IEEE transactions on visualization and computer graphics Pub Date : 2025-05-07 DOI: 10.1109/TVCG.2025.3567989

Dylan Cashman, Mark Keller, Hyeon Jeon, Bum Chul Kwon, Qianwen Wang

{"title":"A Critical Analysis of the Usage of Dimensionality Reduction in Four Domains.","authors":"Dylan Cashman, Mark Keller, Hyeon Jeon, Bum Chul Kwon, Qianwen Wang","doi":"10.1109/TVCG.2025.3567989","DOIUrl":"https://doi.org/10.1109/TVCG.2025.3567989","url":null,"abstract":"Dimensionality reduction is used as an important tool for unraveling the complexities of high-dimensional datasets in many fields of science, such as cell biology, chemical informatics, and physics. Visualizations of the dimensionally-reduced data enable scientists to delve into the intrinsic structures of their datasets and align them with established hypotheses. Visualization researchers have thus proposed many dimensionality reduction methods and interactive systems designed to uncover latent structures. At the same time, different scientific domains have formulated guidelines or common workflows for using dimensionality reduction techniques and visualizations for their respective fields. In this work, we present a critical analysis of the usage of dimensionality reduction in scientific domains outside of computer science. First, we conduct a bibliometric analysis of 21,249 academic publications that use dimensionality reduction to observe differences in the frequency of techniques across fields. Next, we conduct a survey of a 71-paper sample from four fields: biology, chemistry, physics, and business. Through this survey, we uncover common workflows, processes, and usage patterns, including the mixed use of confirmatory data analysis to validate a dataset and projection method and exploratory data analysis to then generate more hypotheses. We also find that misinterpretations and inappropriate usage is common, particularly in the visual interpretation of the resulting dimensionally reduced view. Lastly, we compare our observations with recent works in the visualization community in order to match work within our community to potential areas of impact outside our community. By comparing the usage found within scientific fields to the recent research output of the visualization community, we offer both validation of the progress of visualization research into dimensionality reduction and a call for action to produce techniques that meet the needs of scientific users.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144060621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TexHOI: Reconstructing Textures of 3D Unknown Objects in Monocular Hand-Object Interaction Scenes. TexHOI：在单目手-物体交互场景中重建三维未知物体的纹理。

IEEE transactions on visualization and computer graphics Pub Date : 2025-05-06 DOI: 10.1109/TVCG.2025.3567276

Alakh Aggarwal, Ningna Wang, Xiaohu Guo

{"title":"TexHOI: Reconstructing Textures of 3D Unknown Objects in Monocular Hand-Object Interaction Scenes.","authors":"Alakh Aggarwal, Ningna Wang, Xiaohu Guo","doi":"10.1109/TVCG.2025.3567276","DOIUrl":"https://doi.org/10.1109/TVCG.2025.3567276","url":null,"abstract":"Reconstructing 3D models of dynamic, real-world objects with high-fidelity textures from monocular frame sequences has been a challenging problem in recent years. This difficulty stems from factors such as shadows, indirect illumination, and inaccurate object-pose estimations due to occluding hand-object interactions. To address these challenges, we propose a novel approach that predicts the hand's impact on environmental visibility and indirect illumination on the object's surface albedo. Our method first learns the geometry and low-fidelity texture of the object, hand, and background through composite rendering of radiance fields. Simultaneously, we optimize the hand and object poses to achieve accurate object-pose estimations. We then refine physics-based rendering parameters-including roughness, specularity, albedo, hand visibility, skin color reflections, and environmental illumination-to produce precise albedo, and accurate hand illumination and shadow regions. Our approach surpasses state-of-the-art methods in texture reconstruction and, to the best of our knowledge, is the first to account for hand-object interactions in object texture reconstruction. Please check our work at: https://alakhag.github.io/TexHOI-website/.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144056198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0